SS&C Technologies Holdings, Inc. ($SSNC)

Hi, everyone. Good afternoon. Why don't we go ahead and get started? My name is Jeff Schmitt. I cover wealth management and capital market stocks here at William Blair. I'd like to introduce SS&C Technology, a leading provider of enterprise software and outsourcing solutions for the financial services industry. We're very happy to have them here again this year. And with us, we have the CFO, Brian Schell. So thank you. He's here to discuss the business. And as usual, please go to williamblair.com for a full list of disclosures. So I will turn it over to Brian.

That's a good start. Thanks, Jeff. And again, thanks for a terrific conference and the quality of investors we're meeting with so far. And so we love coming back every year. And thank all of you for joining today. A brief highlight of what I'm going to do today. I'm just going to cover SS&C real quick in case some of you are maybe not as familiar with SS&C, provide an overview, really dig right into AI at the end of the day, right? It's not an extra topic. It's really kind of part and parcel of what we do and the story and what investors want to understand with respect to SS&C. And then I'll just jump to our financial performance and some of the guidance that we put out there. And so with that, we'll just start with the safe harbor, I won't spend much time on this, but it's there, again, big part of the presentations. Jeff, kind of highlighted here is kind of a bit of our kind of high-level description about SS&C as far as kind of where we sit in providing the mission-critical systems, financial services and health care industry. And you'll hear more and more about this as we go through, but it's important to kind of easy to come back to. But specifically, SS&C, why do we own what we own? What do we do what we do is that we believe there's value in the breadth and depth of the services that we offer across the board, right? The services include the transaction process and the accounting, the operations supporting, everything that I'll call it in broad asset managers primarily do, the reporting, the compliance, the analytics and those asset managers, including the asset, the hedge fund industry, banks, insurance companies, private markets, wealth management. And then we do have a technology where we are a provider to the health care industry as well for a small sliver of the organization. Again, that technology solution that we provide is primarily around either the on-prem or cloud software-as-a-service, the actual software itself, outsourcing of operations, as I mentioned earlier, as well as AI, agentic AI and kind of the workflow orchestration, including use of RPA and overall operational execution. As we look at the revenue kind of by business, here's a, like I said, high-level perspective, right? So 3 of our business units cover 75% of the business, right? So you have GlobeOp, you have GIDS and you have the Wealth. And each of those roughly represent about 25% each. And so as these 3 business units go, as well consolidated SS&C goes, a couple of -- the remaining 25% is covered by a couple of horizontals in the form of intelligent automation analytics, which is where we have the RPA and the AI and the agents that we've been deploying as well as Intralinks, which many of you are familiar with, providing a lot of the transaction support services and secure data sites, data rooms. And then the remaining roughly 5%, now actually close to 4% is our health care kind of medical and claims processing group and providing analytics to those users as well. Let's just jump right into AI. And we've had these conversations with some of the one-on-ones earlier today about, is it -- is AI is disrupting our industry? Well, 100% it is, right? That's when we bring it upfront, what are we doing? What does it look like? How does it involve SS&C? And how are we thinking about it? And when we think about disruption, the use in this word is that disruption is definitely meaning change at the end of the day, right? There are some organizations that will not survive as much and will be significantly impacted in the future, others who -- it's either a tailwind or a headwind. And for those that we think it's more of a headwind to it, it's those that we think are convenient Software-as-a-Service. And something that's very vulnerable that could be easily replicated versus we think which is less disruptive, but certainly is impacted and we think as a positive can utilize those changes is we've categorized ourselves in more of that the system of record and how we think about where SS&C sits with its primary services with the proprietary data, the regulatory requirements and everything that we have, we think AI strengthens the offering that we have. Because if you think about what we do versus what AI can enable, there's 4, I'll call it, assets or qualities that we bring, and we call it to the AI cycle. The first is that domain expertise across financial services and health care services, right? There's that accounting logic, that regulatory interpretation, that exception handling that's just comes with time and knowledge that is just not easily replicated or sitting outside some system about how does this work? You can just kind of make up. You have to have the same consistent answer every single time you process the transaction. And that's where the domain experience and expertise and real IP in the broadest sense comes into play. The other is, we call it the stewardship of the client operational data. And here's where we have the continuity of all the data, the information, I mean there's $55 trillion of assets, of client assets that sit on an SS&C software solution, service, our SS&C tech. And knowing this, how it works, this is very difficult to replicate this knowledge at the end of the day. The embedded systems of record, I kind of referenced this right upfront, right? I mean the production systems, the execution, where the work is executed and where does it go from there and what is needed. And that integration with custody systems, integration to the regulators and the reporting is critical overall to operational efficiency and processing overall to meet various compliance standards as well as not just the regulators, but also to the clients of the clients that we serve, most importantly. And then the last part, which we think is one of the most compelling parts, certainly working with our clients is that we take this perspective of being client zero. A lot of this work and these systems, everything start out inside of SS&C's 4 walls is that we test it, we utilize it, we deploy it on ourselves and within our operations to achieve the efficiencies and achieve the outcomes that we essentially will eventually and that we have sell to our clients, both either embedded within the software or as part of the overall operations. So real operational practical knowledge of how it works, what to use in the loop, where does it make sense, the controls and what works and what doesn't. So we think these are, I'll call it, the assets that we bring to the cycle and why at the end of the day, we feel positive and feel that the AI elements and what we can do is really more of a tailwind for us, and we're excited about what it can do to us going forward and our growth rates, both top line and bottom line. Again, a little bit of a backdrop of leading up to this and why do we have these capabilities. A lot of the acquisition work, and this is a summary of some of the larger acquisitions that we've done, really, you see how it expands our network and what we've done of our services, our capability, our distribution, our client base. You can see we've added analytics, trust services. You see the Blue Prism automation, which was really found -- served as a foundation with originally as RPA, moving that to be more AI forward. It really gave us a really jump start overall to incorporate within our own operations. And then the most recent acquisition with Calastone, which also gives us a foothold into tokenization. They already have clients already utilizing tokenization in its operations. Again, nascent, but actually live and actually working right now. But if we want to take a closer look at the agentic AI elements and a little bit of the time line and how we've evolved Blue Prism with the RPA because this is pretty fundamental to our AI strategy going forward is you can see the timeline that we have here and what we've done. This is -- we acquired the business 3 years ago, closer to 4, I guess, now. And you can see how -- that we've made a lot of strategic changes starting last year with what we've done around the business and how we've continued to evolve it from just, I'll call it, a pure RPA play to beginning to make those agents smarter, starting to leverage AI and being able to do more with the agents and being able to connect different processes that it couldn't before. And as we recently launched -- and we've added a lot of talent along the way that was more focused on this AI forward look and utilization of the tools. And when we get to the end of the day and we see the positive, call it, revenue or commercial outcome, the revenue mix within this business unit and within Blue Prism more specifically is we are going to see larger contracts and work that's being done, and it won't necessarily be seat-based type of licensing, to be more outcome-based end usage similar to what you might expect. And so like I said, we're very hopeful. We're in the early stages of it. And as we have more to report, we certainly will, but we do expect larger dollar contracts to start as the commercial outcome of what we're doing here. As we look at our overall approach, now these next couple of slides are a bit dense, and I don't expect -- I'm going to hit every point. So I would encourage you to download the slides, and you can kind of see some of the other metrics that we're looking at. But some things we need to continue to want to reinforce with our investors to understand that this isn't just a -- we're just put AI on the PowerPoint slide and said, we are an AI company is that we are used to, and this is what we do is fundamentally is we spend a lot of money on R&D every year, and more and more money is being deployed utilizing AI across the board. And some of the examples of where we put it in, we put some statistics there in that second chart of the multiplier of looking at how much less time of those developers who started to start using some of these tools reduction in cycle time and reduction of how much time we get a new release to the market and kind of some of the return numbers that we've calculated internally and what this looks like. We talked about the WorkHQ launch, which again is an overall orchestration layer that can be applicable kind of horizontally, primarily financial services firms with clients using our existing software, connecting the AI agents, connecting RPA, connecting digital workers and the core services and software that they're already utilizing and they don't have to rip out their existing systems. And again, a lot of these systems, the expression you probably heard it from others, it's not necessarily greenfield for AI, it's brownfield because AI doesn't work as well working with APIs at the end of the day, right? And agents do. And so leveraging that technology, leveraging that understanding and how everything needs to flow to get to a specific outcome from the client data, we think, gives us a really good advantage in understanding and a tool that a lot of people are going to want. And we're testing it internally, like I said, and we have several clients that we're working with right now in, I'd say, advanced stages. Again, nothing to report yet, but we're very excited about, like I said, the prospects overall. We already have a lot of infrastructure internally to support this with multiple LLMs deployed on-prem globally so that it's -- everything is still within our 4 walls. We call AI gateway to help support that governance structure given that we -- our clients are almost all regulated, some more so than others in different geographies. And so that governance, that auditability, understanding the flow and changes is very important and making sure there's no data leakage or external. So control of that data and use of AI has been very important from day 1. So I am going to mention, again, this is also, I mentioned a dense slide. You can take a closer look at this. But what we want to do is lay out with our -- the 6 different business units that we do have is these are specific examples and capabilities that we're rolling out right now that we actually are utilizing, right, within our GlobeOp business, trade break reconciliation, which takes out a significant amount of time and resources across the millions and millions of trades that are done on a daily business by our clients, right? You look at the invoice processing kind of across the board. You have fraud detection in AML. You have AI product-driven capabilities embedded within products within the WIT business that a lot of the clients, particularly the small to medium-sized RIAs are demanding or make it so that you have those tools where they can deploy it on their own for some of the more sophisticated organizations. And Intralinks has been embedded as a native part of the software in their latest deal center release. So you see a lot of AI-enabled services that are being deployed right now within different businesses, some on the revenue side and some on the expense savings side to create efficiency, increase accuracy and a quicker time to deliver. So we're pretty excited overall about where this goes. With that, I will turn to an overview of our financials. And we'll do this quickly. The high-level metrics here with the adjusted revenues. Again, just looking at first quarter, up almost 9% on revenues. It was about 5% organic growth rate. The strength of the 8.8% was driven by some acquisitions and a positive FX impact. You can see that more drop to the bottom line with EBITDA growth of 10%, operating -- cash flow from operations up another 10%. And you can see margin expansion of EBITDA of about 40 basis points. And we guided roughly high level for the year to about 50. So this is right in line with where our expectations were. And you can see the EPS increase of 14% -- a little over 14% for the quarter. More broadly, as we look at the margins over time, I mentioned the 50 basis points -- or excuse me, 40 basis points for the quarter, but 50 more broadly is we have a pretty high-margin business model. It's pretty consistent. We've delivered 180 basis points over the last 2 years, so fluctuating between 40 to 60 bps. Again, that's balancing driving incremental productivity and efficiency and incremental cash flows to our shareholders, but also with the discipline of redeploying some of that earnings or that efficiency into our structure to allow for longer-term revenue growth rate as well. So we've tried to make sure there's a reinvestment and so that we're reinvesting for the long-term growth of the business and the long-term health of the business as well as delivering incremental efficiency and margin expansion to our shareholders. We think it's important to deliver both over time. I think that's translated to higher earnings over time, right? So you have the higher revenue, you have increasing EBITDA margins. You've got the benefit of a lower expense structure from lower debt as well as rate reductions over this time frame. We've been working really hard on our tax rate to continue to help deliver incremental earnings. And then, of course, the benefit of share buyback continues to help contribute to that EPS growth rate trend over time, 14.3% on a 3-year CAGR using our midpoint guide for '26. One of the core elements that we look at from other metrics has been just basically AUA. This has been a real nice driver for our -- primarily our GlobeOp business. I think during the last call, we talked about this metric and the growth that we've seen over the last several years. And a lot of firms that compete with us don't even have this amount in total, let alone the growth that we've seen in these years. So solid consistent growth continues to help drive this underlying metric for the health of the business and what we do. It also speaks to the -- to our servicing capability to the largest of clients, primarily the hedge funds, private markets and private credit, private equity, and we serve the most sophisticated of these firms across the globe, and we continue to benefit from their success. I mentioned earlier about R&D as an important metric. And on an earlier slide, we talked about the reinvestment and what we do is we've invested $3.2 billion in R&D since '21, right? So that's a very important part of continuing to try and drive that long-term revenue growth rate. So we measure it. We want to be thoughtful. We're looking for an appropriate ROI on it. And again, we think it leads to better long-term results as far as revenue growth and earnings over time. And we think, again, that leads to ultimately cash flow, which is what we can redeploy back to our shareholders. So you've seen the growth rate over time. We've got a little chart on top of it to kind of look at the cash conversion, which is basically the net income we report and the cash flow from operations, continuing to exceed conversion above 100%, and what this does, we think, can lead us to what we believe is an appropriate and shareholder-friendly capital allocation program. This is just a snapshot of Q1, but our priority has traditionally been high-quality M&A transactions that we believe can create shareholder value. You can see the company has a history of doing that. And absent that, we've said very clearly, particularly given where the stock is trading right now, we will prioritize share repurchase. I'm not sure how much else I can say that other than what this chart does. And so I would expect to see this chart throughout the year. Obviously, the dividend has been set. The debt payment that you see here was a mandatory debt payment and everything else is share repurchase activity. And we would expect to continue to see that and have that benefit over time. Turning to guidance a little bit longer term and what we did, this goes back to a couple of years ago from our investor meeting is that we set the medium-term guidance organic growth as a key metric, and we've given a range of 4% to 8% as our core growth rate. And then we'd expect to try and do more as we are active in opportunistic M&A activities. And I think we've demonstrated that over the last year or 2 with respect to some of the recent acquisitions have really done a nice job of adding, call it, 1 to 3 percentage points on the top line. You can see that 4% to 8% organic revenue growth rate. You'll see the '24 through the '26 estimate. We've been squarely in the middle of that, 6.1%, 4.8%; 5.3% is the current midpoint of the guidance. And you can see the different levers and methods that we use to achieve that with the products, with cross-sells. Price increases have been a relatively small component of that, but still there across the organization. And then the other one that we're seeing, I think, benefits of is improving the customer retention and certainly measure that very closely at each business unit level. As far as the M&A and what makes a good M&A or what makes it attractive, obviously, we want that high-level shareholder value accretion. But what are some of the characteristics we look for in a good M&A transaction for us is we want that revenue growth rate of that candidate to be revenue growth rate accretive, ability to leverage the existing client base in either services or geography. And look, we want to see profitable growth at the end of the day, right? We don't want to buy a start-up or no earnings growth business. So those are important things that as part of the target, which makes it very attractive to us. And of course, we've always tried to deploy price discipline as far as what we're willing to pay for a transaction to make sure there's not too much of a valuation gap in our expectations. As far as the quarterly guide goes, again, this is out there. For the second quarter, we put out an organic growth rate midpoint of 5.6%, a little bit higher than obviously than what we guided to for at least for the full year. You'll see the EPS growth rate -- excuse me, EPS number at the midpoint of $1.67, which is a 12% growth rate. For the full year, I'll just flash that up as well, is an organic growth rate midpoint of 5.3%, which is up from 5.1% from the beginning of the year and then EPS midpoint of $6.90, which is about a 12% growth rate, which is kind of what we said as far as the growth rate we've seen that 12% to 14%, depending on which metric you're looking at, and that's where we're landing right now as well as the continued healthy cash flow from operations, which, again, is a key metric from us. So I think that takes me to the end. So we have roughly 5 minutes. Do you want to turn it over to you or...

Yes. feel free to open it up to questions, to see if we have questions. Or else we will go to [Technical Difficulty] First one on AI. Are you seeing that -- I mean here we've been implementing it more recently. But is it enough at this point where you're seeing an impact on the bottom line? Or is there to talk more [Technical Difficulty]

I would say that we are -- the approach that we're taking is a -- we don't need to be bleeding edge certainly. And I'll say that for the couple of, right, is that we're putting it in the hands of our most savvy, both developers as well as those within, for example, like with the finance organization that are not like how do I turn on the computer type of -- we're not going to be having those people, but basically who may have already been using it on their own, I'll call it, the power users already. So we've already seen that efficiency built in. But it's been -- we're trying to be very measured in it before we -- everybody has a Claude license, for example, type of thing. So we're monitoring token usage, trying to measure ROI right away as far as it's taking 2 days to do this and used to take 3 months type of those types of being able to put these enhancements in place. So we are I would say, right now, those productivity gains we're rolling back into, I'll call it, the business and being -- getting more efficiency and productivity to where we see right now, right? So there's 3 paths, I think that very high level around AI is that you can see that path of that technology use and you don't have to hire incremental resources to support more client growth or you're delivering things a little bit more quickly. The second path, which you've seen a lot of announcements in a 5%, 10%, 20% reduction of headcount because I don't need as many people or I'm going to deploy AI. And that third path, which we're starting to see more of is on the revenue delivery of, is it enhancing your revenue sales as a result of utilization of AI. So I would say we're still -- given the $6.7-ish billion of revenues, it's hard to show a huge impact right away on it, but we're starting to see signs of it for sure.

Go ahead.

What are changes that you see in [indiscernible] AI more on, [Technical Difficulty] more often, how do you -- how do you -- you have lots of LLMs, you do have Claude, how do you [Technical Difficulty] make it improve, how does that work from [Technical Difficulty]

So what we do is for the -- so the LLM, no. But for the -- so we don't use the, in a way, I want to answer yes. So we're exploring different ways. So we do bring some on-prem when we're actually putting our data in and exploring that, right? So we put it within our AI framework so that any of the enhancements and the learnings and everything that is does the revisions stays within. So that's not shared back out, okay? And so we do monitor the token usage. If we aren't going external, if we're using it, say, to build code, say, I'm going to use an Opus model that's the frontier model, a frontier model, and I'm going to have to pay for some of that upfront. That's a -- we found a pretty good investment. So learning to use which of the models are great for the brains and which are good for just the muscle, so to speak, that can still do things that the average person can't. And so we're looking for that blend and finding the right blend around those costs. So -- but keeping that data and that learning, particularly what we're doing internally is what we're doing for the actual data when we're running that through is on-prem.

Let's say you actually take [Technical Difficulty]

Well, to the extent that we can, yes, as far as the learning. So if it's generating like, for example, a Python code to be able to do this, we take that code, pop it in, and I don't have to keep tapping the Opus mode because I now have what I want to do would it help me create. So I don't have to keep doing it. It becomes an R&D tool in and of itself.

Thank you, Brian.

Yes. Thanks.